Overview

Brought to you by YData

Dataset statistics

Number of variables60
Number of observations34719
Missing cells625994
Missing cells (%)30.1%
Duplicate rows601
Duplicate rows (%)1.7%
Total size in memory18.1 MiB
Average record size in memory548.0 B

Variable types

Numeric9
Categorical51

Alerts

Dataset has 601 (1.7%) duplicate rowsDuplicates
AMS is highly overall correlated with AMSAgitated and 6 other fieldsHigh correlation
AMSAgitated is highly overall correlated with AMSHigh correlation
AMSOth is highly overall correlated with AMSHigh correlation
AMSRepeat is highly overall correlated with AMSHigh correlation
AMSSleep is highly overall correlated with AMSHigh correlation
AMSSlow is highly overall correlated with AMSHigh correlation
ActNorm is highly overall correlated with AMSHigh correlation
Amnesia_verb is highly overall correlated with LocLenHigh correlation
ClavFro is highly overall correlated with HemaLocHigh correlation
ClavOcc is highly overall correlated with HemaLocHigh correlation
ClavPar is highly overall correlated with HemaLocHigh correlation
GCSEye is highly overall correlated with GCSMotor and 5 other fieldsHigh correlation
GCSMotor is highly overall correlated with GCSEye and 6 other fieldsHigh correlation
GCSTotal is highly overall correlated with GCSEye and 6 other fieldsHigh correlation
GCSVerbal is highly overall correlated with AMS and 6 other fieldsHigh correlation
HASeverity is highly overall correlated with HAStartHigh correlation
HAStart is highly overall correlated with HASeverityHigh correlation
HemaLoc is highly overall correlated with ClavFro and 3 other fieldsHigh correlation
HemaSize is highly overall correlated with HemaLocHigh correlation
High_impact_InjSev is highly overall correlated with InjuryMechHigh correlation
InjuryMech is highly overall correlated with High_impact_InjSevHigh correlation
Intubated is highly overall correlated with GCSEye and 5 other fieldsHigh correlation
LocLen is highly overall correlated with Amnesia_verbHigh correlation
NeuroDReflex is highly overall correlated with GCSMotorHigh correlation
Paralyzed is highly overall correlated with GCSEye and 5 other fieldsHigh correlation
PosIntFinal is highly overall correlated with GCSTotalHigh correlation
SFxBasHem is highly overall correlated with SFxBasOto and 3 other fieldsHigh correlation
SFxBasOto is highly overall correlated with SFxBasHem and 3 other fieldsHigh correlation
SFxBasPer is highly overall correlated with SFxBasHem and 3 other fieldsHigh correlation
SFxBasRet is highly overall correlated with SFxBasHem and 3 other fieldsHigh correlation
SFxBasRhi is highly overall correlated with SFxBasHem and 3 other fieldsHigh correlation
Sedated is highly overall correlated with GCSEye and 5 other fieldsHigh correlation
SeizLen is highly overall correlated with SeizOccurHigh correlation
SeizOccur is highly overall correlated with SeizLenHigh correlation
VomitLast is highly overall correlated with VomitNbr and 1 other fieldsHigh correlation
VomitNbr is highly overall correlated with VomitLast and 1 other fieldsHigh correlation
VomitStart is highly overall correlated with VomitLast and 1 other fieldsHigh correlation
SeizOccur is highly imbalanced (94.1%)Imbalance
VomitNbr is highly imbalanced (65.2%)Imbalance
VomitLast is highly imbalanced (65.4%)Imbalance
Dizzy is highly imbalanced (54.5%)Imbalance
Intubated is highly imbalanced (95.3%)Imbalance
Paralyzed is highly imbalanced (96.9%)Imbalance
Sedated is highly imbalanced (95.4%)Imbalance
GCSEye is highly imbalanced (90.3%)Imbalance
GCSVerbal is highly imbalanced (86.0%)Imbalance
AMSRepeat is highly imbalanced (63.4%)Imbalance
SFxPalpDepress is highly imbalanced (89.7%)Imbalance
FontBulg is highly imbalanced (99.0%)Imbalance
SFxBasHem is highly imbalanced (94.6%)Imbalance
SFxBasOto is highly imbalanced (94.9%)Imbalance
SFxBasPer is highly imbalanced (94.7%)Imbalance
SFxBasRet is highly imbalanced (94.9%)Imbalance
SFxBasRhi is highly imbalanced (95.0%)Imbalance
ClavNeck is highly imbalanced (85.1%)Imbalance
ClavTem is highly imbalanced (68.9%)Imbalance
NeuroDReflex is highly imbalanced (64.1%)Imbalance
NeuroDOth is highly imbalanced (93.8%)Imbalance
OSICut is highly imbalanced (90.1%)Imbalance
OSIPelvis is highly imbalanced (72.3%)Imbalance
OSIOth is highly imbalanced (57.7%)Imbalance
Drugs is highly imbalanced (91.4%)Imbalance
PosIntFinal is highly imbalanced (86.9%)Imbalance
ClavOth is highly imbalanced (93.9%)Imbalance
Amnesia_verb has 13686 (39.4%) missing valuesMissing
LocLen has 1507 (4.3%) missing valuesMissing
SeizOccur has 737 (2.1%) missing valuesMissing
SeizLen has 737 (2.1%) missing valuesMissing
ActNorm has 2685 (7.7%) missing valuesMissing
HASeverity has 11780 (33.9%) missing valuesMissing
HAStart has 11780 (33.9%) missing valuesMissing
VomitNbr has 361 (1.0%) missing valuesMissing
VomitStart has 361 (1.0%) missing valuesMissing
VomitLast has 361 (1.0%) missing valuesMissing
Dizzy has 12797 (36.9%) missing valuesMissing
GCSEye has 1021 (2.9%) missing valuesMissing
GCSVerbal has 1032 (3.0%) missing valuesMissing
GCSMotor has 1041 (3.0%) missing valuesMissing
AMSAgitated has 29544 (85.1%) missing valuesMissing
AMSSleep has 29544 (85.1%) missing valuesMissing
AMSSlow has 29544 (85.1%) missing valuesMissing
AMSRepeat has 29544 (85.1%) missing valuesMissing
AMSOth has 29544 (85.1%) missing valuesMissing
SFxBasHem has 351 (1.0%) missing valuesMissing
SFxBasOto has 351 (1.0%) missing valuesMissing
SFxBasPer has 351 (1.0%) missing valuesMissing
SFxBasRet has 351 (1.0%) missing valuesMissing
SFxBasRhi has 351 (1.0%) missing valuesMissing
ClavFace has 12424 (35.8%) missing valuesMissing
ClavNeck has 12424 (35.8%) missing valuesMissing
ClavFro has 12424 (35.8%) missing valuesMissing
ClavOcc has 12424 (35.8%) missing valuesMissing
ClavPar has 12424 (35.8%) missing valuesMissing
ClavTem has 12424 (35.8%) missing valuesMissing
NeuroDMotor has 34177 (98.4%) missing valuesMissing
NeuroDSensory has 34177 (98.4%) missing valuesMissing
NeuroDCranial has 34177 (98.4%) missing valuesMissing
NeuroDReflex has 34177 (98.4%) missing valuesMissing
OSIExtremity has 31064 (89.5%) missing valuesMissing
OSICut has 31064 (89.5%) missing valuesMissing
OSICspine has 31064 (89.5%) missing valuesMissing
OSIFlank has 31064 (89.5%) missing valuesMissing
OSIAbdomen has 31064 (89.5%) missing valuesMissing
OSIPelvis has 31064 (89.5%) missing valuesMissing
Drugs has 1460 (4.2%) missing valuesMissing
Ethnicity has 12790 (36.8%) missing valuesMissing
Race has 2547 (7.3%) missing valuesMissing
LocLen has 27752 (79.9%) zerosZeros
SeizLen has 33486 (96.4%) zerosZeros
HAStart has 12696 (36.6%) zerosZeros
VomitStart has 29711 (85.6%) zerosZeros

Reproduction

Analysis started2025-02-20 15:29:46.633175
Analysis finished2025-02-20 15:30:50.418735
Duration1 minute and 3.79 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

InjuryMech
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7581728
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:30:50.678051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median8
Q310
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3782667
Coefficient of variation (CV)0.43544618
Kurtosis-0.41106988
Mean7.7581728
Median Absolute Deviation (MAD)2
Skewness-0.44064247
Sum269356
Variance11.412686
MonotonicityNot monotonic
2025-02-20T07:30:51.049617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8 9574
27.6%
6 3786
 
10.9%
1 3101
 
8.9%
13 3004
 
8.7%
12 2515
 
7.2%
11 2460
 
7.1%
10 2357
 
6.8%
9 2287
 
6.6%
7 1979
 
5.7%
4 1350
 
3.9%
Other values (3) 2306
 
6.6%
ValueCountFrequency (%)
1 3101
 
8.9%
2 1129
 
3.3%
3 447
 
1.3%
4 1350
 
3.9%
5 730
 
2.1%
6 3786
 
10.9%
7 1979
 
5.7%
8 9574
27.6%
9 2287
 
6.6%
10 2357
 
6.8%
ValueCountFrequency (%)
13 3004
 
8.7%
12 2515
 
7.2%
11 2460
 
7.1%
10 2357
 
6.8%
9 2287
 
6.6%
8 9574
27.6%
7 1979
 
5.7%
6 3786
 
10.9%
5 730
 
2.1%
4 1350
 
3.9%

High_impact_InjSev
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing271
Missing (%)0.8%
Memory size542.5 KiB
2
23386 
1
5765 
3
5297 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34448
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23386
67.4%
1 5765
 
16.6%
3 5297
 
15.3%
(Missing) 271
 
0.8%

Length

2025-02-20T07:30:51.461227image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:51.799739image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
2 23386
67.9%
1 5765
 
16.7%
3 5297
 
15.4%

Most occurring characters

ValueCountFrequency (%)
2 23386
67.9%
1 5765
 
16.7%
3 5297
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23386
67.9%
1 5765
 
16.7%
3 5297
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23386
67.9%
1 5765
 
16.7%
3 5297
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23386
67.9%
1 5765
 
16.7%
3 5297
 
15.4%

Amnesia_verb
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing13686
Missing (%)39.4%
Memory size542.5 KiB
0
17399 
1
3634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21033
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 17399
50.1%
1 3634
 
10.5%
(Missing) 13686
39.4%

Length

2025-02-20T07:30:52.155463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:52.493629image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17399
82.7%
1 3634
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 17399
82.7%
1 3634
 
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17399
82.7%
1 3634
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17399
82.7%
1 3634
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17399
82.7%
1 3634
 
17.3%

LocLen
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing1507
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean0.53601108
Minimum0
Maximum5
Zeros27752
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:30:52.772125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3556854
Coefficient of variation (CV)2.5292116
Kurtosis4.9921002
Mean0.53601108
Median Absolute Deviation (MAD)0
Skewness2.5136108
Sum17802
Variance1.837883
MonotonicityNot monotonic
2025-02-20T07:30:53.109367image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 27752
79.9%
5 2044
 
5.9%
2 1488
 
4.3%
3 895
 
2.6%
1 737
 
2.1%
4 296
 
0.9%
(Missing) 1507
 
4.3%
ValueCountFrequency (%)
0 27752
79.9%
1 737
 
2.1%
2 1488
 
4.3%
3 895
 
2.6%
4 296
 
0.9%
5 2044
 
5.9%
ValueCountFrequency (%)
5 2044
 
5.9%
4 296
 
0.9%
3 895
 
2.6%
2 1488
 
4.3%
1 737
 
2.1%
0 27752
79.9%

SeizOccur
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing737
Missing (%)2.1%
Memory size542.5 KiB
0
33486 
1
 
207
2
 
168
4
 
62
3
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33982
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33486
96.4%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%
(Missing) 737
 
2.1%

Length

2025-02-20T07:30:53.506759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:53.875337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 33486
98.5%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 33486
98.5%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33486
98.5%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33486
98.5%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33486
98.5%
1 207
 
0.6%
2 168
 
0.5%
4 62
 
0.2%
3 59
 
0.2%

SeizLen
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing737
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.033164617
Minimum0
Maximum5
Zeros33486
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:30:54.165614image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3282606
Coefficient of variation (CV)9.8979165
Kurtosis167.56808
Mean0.033164617
Median Absolute Deviation (MAD)0
Skewness12.340046
Sum1127
Variance0.10775502
MonotonicityNot monotonic
2025-02-20T07:30:54.425764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 33486
96.4%
1 213
 
0.6%
2 145
 
0.4%
5 97
 
0.3%
3 25
 
0.1%
4 16
 
< 0.1%
(Missing) 737
 
2.1%
ValueCountFrequency (%)
0 33486
96.4%
1 213
 
0.6%
2 145
 
0.4%
3 25
 
0.1%
4 16
 
< 0.1%
5 97
 
0.3%
ValueCountFrequency (%)
5 97
 
0.3%
4 16
 
< 0.1%
3 25
 
0.1%
2 145
 
0.4%
1 213
 
0.6%
0 33486
96.4%

ActNorm
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2685
Missing (%)7.7%
Memory size542.5 KiB
1
26639 
0
5395 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32034
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 26639
76.7%
0 5395
 
15.5%
(Missing) 2685
 
7.7%

Length

2025-02-20T07:30:54.817499image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:55.100886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 26639
83.2%
0 5395
 
16.8%

Most occurring characters

ValueCountFrequency (%)
1 26639
83.2%
0 5395
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 26639
83.2%
0 5395
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 26639
83.2%
0 5395
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 26639
83.2%
0 5395
 
16.8%

HASeverity
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing11780
Missing (%)33.9%
Memory size542.5 KiB
0
12696 
2
4563 
1
4170 
4
 
842
3
 
668

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22939
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 12696
36.6%
2 4563
 
13.1%
1 4170
 
12.0%
4 842
 
2.4%
3 668
 
1.9%
(Missing) 11780
33.9%

Length

2025-02-20T07:30:55.432186image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:55.774188image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12696
55.3%
2 4563
 
19.9%
1 4170
 
18.2%
4 842
 
3.7%
3 668
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 12696
55.3%
2 4563
 
19.9%
1 4170
 
18.2%
4 842
 
3.7%
3 668
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12696
55.3%
2 4563
 
19.9%
1 4170
 
18.2%
4 842
 
3.7%
3 668
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12696
55.3%
2 4563
 
19.9%
1 4170
 
18.2%
4 842
 
3.7%
3 668
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12696
55.3%
2 4563
 
19.9%
1 4170
 
18.2%
4 842
 
3.7%
3 668
 
2.9%

HAStart
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing11780
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean1.0633855
Minimum0
Maximum5
Zeros12696
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:30:56.054521image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3431256
Coefficient of variation (CV)1.2630655
Kurtosis0.96666453
Mean1.0633855
Median Absolute Deviation (MAD)0
Skewness1.1491726
Sum24393
Variance1.8039863
MonotonicityNot monotonic
2025-02-20T07:30:56.465429image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 12696
36.6%
2 8463
24.4%
5 1066
 
3.1%
3 506
 
1.5%
4 137
 
0.4%
1 71
 
0.2%
(Missing) 11780
33.9%
ValueCountFrequency (%)
0 12696
36.6%
1 71
 
0.2%
2 8463
24.4%
3 506
 
1.5%
4 137
 
0.4%
5 1066
 
3.1%
ValueCountFrequency (%)
5 1066
 
3.1%
4 137
 
0.4%
3 506
 
1.5%
2 8463
24.4%
1 71
 
0.2%
0 12696
36.6%

VomitNbr
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing361
Missing (%)1.0%
Memory size542.5 KiB
0
29711 
3
 
1739
1
 
1730
2
 
931
4
 
247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34358
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 29711
85.6%
3 1739
 
5.0%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%
(Missing) 361
 
1.0%

Length

2025-02-20T07:30:56.903362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:57.279331image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 29711
86.5%
3 1739
 
5.1%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 29711
86.5%
3 1739
 
5.1%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
3 1739
 
5.1%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
3 1739
 
5.1%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
3 1739
 
5.1%
1 1730
 
5.0%
2 931
 
2.7%
4 247
 
0.7%

VomitStart
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing361
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.36343792
Minimum0
Maximum5
Zeros29711
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:30:57.675462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98125088
Coefficient of variation (CV)2.6999133
Kurtosis6.9799485
Mean0.36343792
Median Absolute Deviation (MAD)0
Skewness2.7524033
Sum12487
Variance0.9628533
MonotonicityNot monotonic
2025-02-20T07:30:58.072822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 29711
85.6%
2 2497
 
7.2%
3 1267
 
3.6%
4 503
 
1.4%
5 325
 
0.9%
1 55
 
0.2%
(Missing) 361
 
1.0%
ValueCountFrequency (%)
0 29711
85.6%
1 55
 
0.2%
2 2497
 
7.2%
3 1267
 
3.6%
4 503
 
1.4%
5 325
 
0.9%
ValueCountFrequency (%)
5 325
 
0.9%
4 503
 
1.4%
3 1267
 
3.6%
2 2497
 
7.2%
1 55
 
0.2%
0 29711
85.6%

VomitLast
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing361
Missing (%)1.0%
Memory size542.5 KiB
0
29711 
1
 
2194
2
 
1367
4
 
802
3
 
284

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34358
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 29711
85.6%
1 2194
 
6.3%
2 1367
 
3.9%
4 802
 
2.3%
3 284
 
0.8%
(Missing) 361
 
1.0%

Length

2025-02-20T07:30:58.533635image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:58.912049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 29711
86.5%
1 2194
 
6.4%
2 1367
 
4.0%
4 802
 
2.3%
3 284
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 29711
86.5%
1 2194
 
6.4%
2 1367
 
4.0%
4 802
 
2.3%
3 284
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
1 2194
 
6.4%
2 1367
 
4.0%
4 802
 
2.3%
3 284
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
1 2194
 
6.4%
2 1367
 
4.0%
4 802
 
2.3%
3 284
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29711
86.5%
1 2194
 
6.4%
2 1367
 
4.0%
4 802
 
2.3%
3 284
 
0.8%

Dizzy
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12797
Missing (%)36.9%
Memory size542.5 KiB
0
19826 
1
2096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21922
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 19826
57.1%
1 2096
 
6.0%
(Missing) 12797
36.9%

Length

2025-02-20T07:30:59.299302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:30:59.688040image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19826
90.4%
1 2096
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 19826
90.4%
1 2096
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19826
90.4%
1 2096
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19826
90.4%
1 2096
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19826
90.4%
1 2096
 
9.6%

Intubated
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing273
Missing (%)0.8%
Memory size542.5 KiB
0
34266 
1
 
180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34446
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34266
98.7%
1 180
 
0.5%
(Missing) 273
 
0.8%

Length

2025-02-20T07:31:00.185415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:00.503733image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34266
99.5%
1 180
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 34266
99.5%
1 180
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34266
99.5%
1 180
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34266
99.5%
1 180
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34266
99.5%
1 180
 
0.5%

Paralyzed
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing283
Missing (%)0.8%
Memory size542.5 KiB
0
34325 
1
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34436
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34325
98.9%
1 111
 
0.3%
(Missing) 283
 
0.8%

Length

2025-02-20T07:31:00.913662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:01.287050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34325
99.7%
1 111
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 34325
99.7%
1 111
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34325
99.7%
1 111
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34325
99.7%
1 111
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34325
99.7%
1 111
 
0.3%

Sedated
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing286
Missing (%)0.8%
Memory size542.5 KiB
0
34258 
1
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34433
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34258
98.7%
1 175
 
0.5%
(Missing) 286
 
0.8%

Length

2025-02-20T07:31:01.630090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:01.996785image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34258
99.5%
1 175
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 34258
99.5%
1 175
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34258
99.5%
1 175
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34258
99.5%
1 175
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34258
99.5%
1 175
 
0.5%

GCSEye
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1021
Missing (%)2.9%
Memory size542.5 KiB
4
32912 
3
 
415
1
 
236
2
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33698
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 32912
94.8%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%
(Missing) 1021
 
2.9%

Length

2025-02-20T07:31:02.416741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:02.799939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
4 32912
97.7%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%

Most occurring characters

ValueCountFrequency (%)
4 32912
97.7%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 32912
97.7%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 32912
97.7%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 32912
97.7%
3 415
 
1.2%
1 236
 
0.7%
2 135
 
0.4%

GCSVerbal
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1032
Missing (%)3.0%
Memory size542.5 KiB
5
32139 
4
 
1137
1
 
244
3
 
91
2
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33687
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 32139
92.6%
4 1137
 
3.3%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%
(Missing) 1032
 
3.0%

Length

2025-02-20T07:31:03.200069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:03.645501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
5 32139
95.4%
4 1137
 
3.4%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5 32139
95.4%
4 1137
 
3.4%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 32139
95.4%
4 1137
 
3.4%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 32139
95.4%
4 1137
 
3.4%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 32139
95.4%
4 1137
 
3.4%
1 244
 
0.7%
3 91
 
0.3%
2 76
 
0.2%

GCSMotor
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing1041
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean5.9584001
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:31:03.978318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37685191
Coefficient of variation (CV)0.063247163
Kurtosis135.53442
Mean5.9584001
Median Absolute Deviation (MAD)0
Skewness-11.214579
Sum200667
Variance0.14201736
MonotonicityNot monotonic
2025-02-20T07:31:04.388123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 33068
95.2%
5 304
 
0.9%
1 142
 
0.4%
4 123
 
0.4%
3 23
 
0.1%
2 18
 
0.1%
(Missing) 1041
 
3.0%
ValueCountFrequency (%)
1 142
 
0.4%
2 18
 
0.1%
3 23
 
0.1%
4 123
 
0.4%
5 304
 
0.9%
6 33068
95.2%
ValueCountFrequency (%)
6 33068
95.2%
5 304
 
0.9%
4 123
 
0.4%
3 23
 
0.1%
2 18
 
0.1%
1 142
 
0.4%

GCSTotal
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.839713
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:31:04.704174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14
Q115
median15
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0371436
Coefficient of variation (CV)0.069889736
Kurtosis90.121197
Mean14.839713
Median Absolute Deviation (MAD)0
Skewness-9.0868882
Sum515220
Variance1.0756669
MonotonicityNot monotonic
2025-02-20T07:31:05.608268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 32863
94.7%
14 1079
 
3.1%
13 246
 
0.7%
3 141
 
0.4%
12 76
 
0.2%
11 75
 
0.2%
9 48
 
0.1%
10 44
 
0.1%
6 41
 
0.1%
8 38
 
0.1%
Other values (3) 68
 
0.2%
ValueCountFrequency (%)
3 141
0.4%
4 19
 
0.1%
5 15
 
< 0.1%
6 41
 
0.1%
7 34
 
0.1%
8 38
 
0.1%
9 48
 
0.1%
10 44
 
0.1%
11 75
0.2%
12 76
0.2%
ValueCountFrequency (%)
15 32863
94.7%
14 1079
 
3.1%
13 246
 
0.7%
12 76
 
0.2%
11 75
 
0.2%
10 44
 
0.1%
9 48
 
0.1%
8 38
 
0.1%
7 34
 
0.1%
6 41
 
0.1%

AMS
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing259
Missing (%)0.7%
Memory size542.5 KiB
0
29285 
1
5175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29285
84.3%
1 5175
 
14.9%
(Missing) 259
 
0.7%

Length

2025-02-20T07:31:05.810422image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:05.984729image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 29285
85.0%
1 5175
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 29285
85.0%
1 5175
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29285
85.0%
1 5175
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29285
85.0%
1 5175
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29285
85.0%
1 5175
 
15.0%

AMSAgitated
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing29544
Missing (%)85.1%
Memory size542.5 KiB
0
4398 
1
777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4398
 
12.7%
1 777
 
2.2%
(Missing) 29544
85.1%

Length

2025-02-20T07:31:06.189807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:06.363832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4398
85.0%
1 777
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 4398
85.0%
1 777
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4398
85.0%
1 777
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4398
85.0%
1 777
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4398
85.0%
1 777
 
15.0%

AMSSleep
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing29544
Missing (%)85.1%
Memory size542.5 KiB
0
2588 
1
2587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2588
 
7.5%
1 2587
 
7.5%
(Missing) 29544
85.1%

Length

2025-02-20T07:31:06.579604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:06.759742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2588
50.0%
1 2587
50.0%

Most occurring characters

ValueCountFrequency (%)
0 2588
50.0%
1 2587
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2588
50.0%
1 2587
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2588
50.0%
1 2587
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2588
50.0%
1 2587
50.0%

AMSSlow
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing29544
Missing (%)85.1%
Memory size542.5 KiB
0
3840 
1
1335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3840
 
11.1%
1 1335
 
3.8%
(Missing) 29544
85.1%

Length

2025-02-20T07:31:06.927446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:07.088259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3840
74.2%
1 1335
 
25.8%

Most occurring characters

ValueCountFrequency (%)
0 3840
74.2%
1 1335
 
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3840
74.2%
1 1335
 
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3840
74.2%
1 1335
 
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3840
74.2%
1 1335
 
25.8%

AMSRepeat
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing29544
Missing (%)85.1%
Memory size542.5 KiB
0
4812 
1
 
363

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4812
 
13.9%
1 363
 
1.0%
(Missing) 29544
85.1%

Length

2025-02-20T07:31:07.341569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:07.509575image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4812
93.0%
1 363
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 4812
93.0%
1 363
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4812
93.0%
1 363
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4812
93.0%
1 363
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4812
93.0%
1 363
 
7.0%

AMSOth
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing29544
Missing (%)85.1%
Memory size542.5 KiB
0
4391 
1
784 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4391
 
12.6%
1 784
 
2.3%
(Missing) 29544
85.1%

Length

2025-02-20T07:31:07.670403image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:07.831046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4391
84.9%
1 784
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 4391
84.9%
1 784
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4391
84.9%
1 784
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4391
84.9%
1 784
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4391
84.9%
1 784
 
15.1%

SFxPalpDepress
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing82
Missing (%)0.2%
Memory size542.5 KiB
0
33663 
3
 
834
2
 
94
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34637
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33663
97.0%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%
(Missing) 82
 
0.2%

Length

2025-02-20T07:31:08.021135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:08.202279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 33663
97.2%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 33663
97.2%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33663
97.2%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33663
97.2%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33663
97.2%
3 834
 
2.4%
2 94
 
0.3%
1 46
 
0.1%

FontBulg
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing139
Missing (%)0.4%
Memory size542.5 KiB
0
34550 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34580
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34550
99.5%
1 30
 
0.1%
(Missing) 139
 
0.4%

Length

2025-02-20T07:31:08.393593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:08.562551image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34550
99.9%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34550
99.9%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34550
99.9%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34550
99.9%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34550
99.9%
1 30
 
0.1%

SFxBasHem
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing351
Missing (%)1.0%
Memory size542.5 KiB
0
34047 
2
 
171
1
 
150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34047
98.1%
2 171
 
0.5%
1 150
 
0.4%
(Missing) 351
 
1.0%

Length

2025-02-20T07:31:08.799386image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:09.090969image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34047
99.1%
2 171
 
0.5%
1 150
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 34047
99.1%
2 171
 
0.5%
1 150
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
2 171
 
0.5%
1 150
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
2 171
 
0.5%
1 150
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
2 171
 
0.5%
1 150
 
0.4%

SFxBasOto
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing351
Missing (%)1.0%
Memory size542.5 KiB
0
34047 
1
 
290
2
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34047
98.1%
1 290
 
0.8%
2 31
 
0.1%
(Missing) 351
 
1.0%

Length

2025-02-20T07:31:09.305134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:09.561883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34047
99.1%
1 290
 
0.8%
2 31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34047
99.1%
1 290
 
0.8%
2 31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 290
 
0.8%
2 31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 290
 
0.8%
2 31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 290
 
0.8%
2 31
 
0.1%

SFxBasPer
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing351
Missing (%)1.0%
Memory size542.5 KiB
0
34047 
1
 
235
2
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34047
98.1%
1 235
 
0.7%
2 86
 
0.2%
(Missing) 351
 
1.0%

Length

2025-02-20T07:31:09.793097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:10.061065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34047
99.1%
1 235
 
0.7%
2 86
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 34047
99.1%
1 235
 
0.7%
2 86
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 235
 
0.7%
2 86
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 235
 
0.7%
2 86
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 235
 
0.7%
2 86
 
0.3%

SFxBasRet
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing351
Missing (%)1.0%
Memory size542.5 KiB
0
34047 
1
 
287
2
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34047
98.1%
1 287
 
0.8%
2 34
 
0.1%
(Missing) 351
 
1.0%

Length

2025-02-20T07:31:10.406864image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:10.705170image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34047
99.1%
1 287
 
0.8%
2 34
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34047
99.1%
1 287
 
0.8%
2 34
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 287
 
0.8%
2 34
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 287
 
0.8%
2 34
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 287
 
0.8%
2 34
 
0.1%

SFxBasRhi
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing351
Missing (%)1.0%
Memory size542.5 KiB
0
34047 
1
 
301
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34047
98.1%
1 301
 
0.9%
2 20
 
0.1%
(Missing) 351
 
1.0%

Length

2025-02-20T07:31:11.040043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:11.256397image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34047
99.1%
1 301
 
0.9%
2 20
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34047
99.1%
1 301
 
0.9%
2 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 301
 
0.9%
2 20
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 301
 
0.9%
2 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34047
99.1%
1 301
 
0.9%
2 20
 
0.1%

HemaLoc
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing245
Missing (%)0.7%
Memory size542.5 KiB
0
20789 
1
7099 
3
3538 
2
2877 
4
 
171

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34474
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 20789
59.9%
1 7099
 
20.4%
3 3538
 
10.2%
2 2877
 
8.3%
4 171
 
0.5%
(Missing) 245
 
0.7%

Length

2025-02-20T07:31:11.499535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:11.768343image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20789
60.3%
1 7099
 
20.6%
3 3538
 
10.3%
2 2877
 
8.3%
4 171
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 20789
60.3%
1 7099
 
20.6%
3 3538
 
10.3%
2 2877
 
8.3%
4 171
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
1 7099
 
20.6%
3 3538
 
10.3%
2 2877
 
8.3%
4 171
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
1 7099
 
20.6%
3 3538
 
10.3%
2 2877
 
8.3%
4 171
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
1 7099
 
20.6%
3 3538
 
10.3%
2 2877
 
8.3%
4 171
 
0.5%

HemaSize
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing245
Missing (%)0.7%
Memory size542.5 KiB
0
20789 
2
7700 
1
2798 
3
2598 
4
 
589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34474
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 20789
59.9%
2 7700
 
22.2%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%
(Missing) 245
 
0.7%

Length

2025-02-20T07:31:12.101079image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:12.393771image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20789
60.3%
2 7700
 
22.3%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 20789
60.3%
2 7700
 
22.3%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
2 7700
 
22.3%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
2 7700
 
22.3%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20789
60.3%
2 7700
 
22.3%
1 2798
 
8.1%
3 2598
 
7.5%
4 589
 
1.7%

ClavFace
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
11789 
1
10506 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 11789
34.0%
1 10506
30.3%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:12.654052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:12.897687image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11789
52.9%
1 10506
47.1%

Most occurring characters

ValueCountFrequency (%)
0 11789
52.9%
1 10506
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11789
52.9%
1 10506
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11789
52.9%
1 10506
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11789
52.9%
1 10506
47.1%

ClavNeck
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
21819 
1
 
476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21819
62.8%
1 476
 
1.4%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:13.177733image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:13.359380image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21819
97.9%
1 476
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 21819
97.9%
1 476
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21819
97.9%
1 476
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21819
97.9%
1 476
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21819
97.9%
1 476
 
2.1%

ClavFro
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
15127 
1
7168 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15127
43.6%
1 7168
20.6%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:13.612679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:13.842324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15127
67.8%
1 7168
32.2%

Most occurring characters

ValueCountFrequency (%)
0 15127
67.8%
1 7168
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15127
67.8%
1 7168
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15127
67.8%
1 7168
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15127
67.8%
1 7168
32.2%

ClavOcc
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
18928 
1
3367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18928
54.5%
1 3367
 
9.7%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:14.071187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:14.316913image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18928
84.9%
1 3367
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 18928
84.9%
1 3367
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18928
84.9%
1 3367
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18928
84.9%
1 3367
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18928
84.9%
1 3367
 
15.1%

ClavPar
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
19547 
1
2748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19547
56.3%
1 2748
 
7.9%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:14.554878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:14.777743image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19547
87.7%
1 2748
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 19547
87.7%
1 2748
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19547
87.7%
1 2748
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19547
87.7%
1 2748
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19547
87.7%
1 2748
 
12.3%

ClavTem
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12424
Missing (%)35.8%
Memory size542.5 KiB
0
21051 
1
 
1244

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21051
60.6%
1 1244
 
3.6%
(Missing) 12424
35.8%

Length

2025-02-20T07:31:15.012501image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:15.250985image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21051
94.4%
1 1244
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 21051
94.4%
1 1244
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21051
94.4%
1 1244
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21051
94.4%
1 1244
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21051
94.4%
1 1244
 
5.6%

NeuroDMotor
Categorical

MISSING 

Distinct2
Distinct (%)0.4%
Missing34177
Missing (%)98.4%
Memory size542.5 KiB
0
393 
1
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters542
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 393
 
1.1%
1 149
 
0.4%
(Missing) 34177
98.4%

Length

2025-02-20T07:31:15.464185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:15.659849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 393
72.5%
1 149
 
27.5%

Most occurring characters

ValueCountFrequency (%)
0 393
72.5%
1 149
 
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 393
72.5%
1 149
 
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 393
72.5%
1 149
 
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 393
72.5%
1 149
 
27.5%

NeuroDSensory
Categorical

MISSING 

Distinct2
Distinct (%)0.4%
Missing34177
Missing (%)98.4%
Memory size542.5 KiB
0
428 
1
114 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters542
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 428
 
1.2%
1 114
 
0.3%
(Missing) 34177
98.4%

Length

2025-02-20T07:31:15.891435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:16.089121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 428
79.0%
1 114
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 428
79.0%
1 114
 
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 428
79.0%
1 114
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 428
79.0%
1 114
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 428
79.0%
1 114
 
21.0%

NeuroDCranial
Categorical

MISSING 

Distinct2
Distinct (%)0.4%
Missing34177
Missing (%)98.4%
Memory size542.5 KiB
0
424 
1
118 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters542
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 424
 
1.2%
1 118
 
0.3%
(Missing) 34177
98.4%

Length

2025-02-20T07:31:16.299365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:16.482446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 424
78.2%
1 118
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 424
78.2%
1 118
 
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 424
78.2%
1 118
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 424
78.2%
1 118
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 424
78.2%
1 118
 
21.8%

NeuroDReflex
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing34177
Missing (%)98.4%
Memory size542.5 KiB
0
505 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters542
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 505
 
1.5%
1 37
 
0.1%
(Missing) 34177
98.4%

Length

2025-02-20T07:31:16.707773image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:16.890211image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 505
93.2%
1 37
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 505
93.2%
1 37
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 505
93.2%
1 37
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 505
93.2%
1 37
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 505
93.2%
1 37
 
6.8%

NeuroDOth
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size542.5 KiB
1
34466 
0
 
253

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

Length

2025-02-20T07:31:17.108202image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:17.334953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 34466
99.3%
0 253
 
0.7%

OSIExtremity
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
1
1980 
0
1675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1980
 
5.7%
0 1675
 
4.8%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:17.572897image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:17.775391image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1980
54.2%
0 1675
45.8%

Most occurring characters

ValueCountFrequency (%)
1 1980
54.2%
0 1675
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1980
54.2%
0 1675
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1980
54.2%
0 1675
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1980
54.2%
0 1675
45.8%

OSICut
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
0
3608 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3608
 
10.4%
1 47
 
0.1%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:18.100009image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:18.334488image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3608
98.7%
1 47
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 3608
98.7%
1 47
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3608
98.7%
1 47
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3608
98.7%
1 47
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3608
98.7%
1 47
 
1.3%

OSICspine
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
0
3174 
1
481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3174
 
9.1%
1 481
 
1.4%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:18.569077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:18.912398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3174
86.8%
1 481
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 3174
86.8%
1 481
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3174
86.8%
1 481
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3174
86.8%
1 481
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3174
86.8%
1 481
 
13.2%

OSIFlank
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
0
3090 
1
565 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3090
 
8.9%
1 565
 
1.6%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:19.121128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:19.451674image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3090
84.5%
1 565
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 3090
84.5%
1 565
 
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3090
84.5%
1 565
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3090
84.5%
1 565
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3090
84.5%
1 565
 
15.5%

OSIAbdomen
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
0
3181 
1
474 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3181
 
9.2%
1 474
 
1.4%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:19.773240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:20.047533image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3181
87.0%
1 474
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 3181
87.0%
1 474
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3181
87.0%
1 474
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3181
87.0%
1 474
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3181
87.0%
1 474
 
13.0%

OSIPelvis
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing31064
Missing (%)89.5%
Memory size542.5 KiB
0
3480 
1
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3655
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3480
 
10.0%
1 175
 
0.5%
(Missing) 31064
89.5%

Length

2025-02-20T07:31:20.281795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:20.671741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3480
95.2%
1 175
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 3480
95.2%
1 175
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3480
95.2%
1 175
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3480
95.2%
1 175
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3480
95.2%
1 175
 
4.8%

OSIOth
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size542.5 KiB
1
31732 
0
 
2987

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Length

2025-02-20T07:31:20.998574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:21.359114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Most occurring characters

ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 31732
91.4%
0 2987
 
8.6%

Drugs
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1460
Missing (%)4.2%
Memory size542.5 KiB
0
32898 
1
 
361

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33259
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32898
94.8%
1 361
 
1.0%
(Missing) 1460
 
4.2%

Length

2025-02-20T07:31:21.726526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:22.874167image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 32898
98.9%
1 361
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 32898
98.9%
1 361
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32898
98.9%
1 361
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32898
98.9%
1 361
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32898
98.9%
1 361
 
1.1%

AgeInMonth
Real number (ℝ)

Distinct216
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.597108
Minimum0
Maximum215
Zeros254
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:31:23.075339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q123
median68
Q3145
95-th percentile200
Maximum215
Range215
Interquartile range (IQR)122

Descriptive statistics

Standard deviation66.655804
Coefficient of variation (CV)0.78792059
Kurtosis-1.194728
Mean84.597108
Median Absolute Deviation (MAD)52
Skewness0.45121728
Sum2937127
Variance4442.9962
MonotonicityNot monotonic
2025-02-20T07:31:23.203989image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 412
 
1.2%
6 411
 
1.2%
7 399
 
1.1%
11 399
 
1.1%
8 397
 
1.1%
14 392
 
1.1%
1 388
 
1.1%
10 384
 
1.1%
15 381
 
1.1%
18 379
 
1.1%
Other values (206) 30777
88.6%
ValueCountFrequency (%)
0 254
0.7%
1 388
1.1%
2 317
0.9%
3 310
0.9%
4 348
1.0%
5 368
1.1%
6 411
1.2%
7 399
1.1%
8 397
1.1%
9 412
1.2%
ValueCountFrequency (%)
215 120
0.3%
214 107
0.3%
213 110
0.3%
212 101
0.3%
211 118
0.3%
210 114
0.3%
209 107
0.3%
208 97
0.3%
207 113
0.3%
206 122
0.4%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size542.5 KiB
1
21636 
2
13080 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34716
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%
(Missing) 3
 
< 0.1%

Length

2025-02-20T07:31:23.291208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:23.360677image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%

Most occurring characters

ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21636
62.3%
2 13080
37.7%

Ethnicity
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing12790
Missing (%)36.8%
Memory size542.5 KiB
2
17680 
1
4249 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21929
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 17680
50.9%
1 4249
 
12.2%
(Missing) 12790
36.8%

Length

2025-02-20T07:31:23.435877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:23.509475image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
2 17680
80.6%
1 4249
 
19.4%

Most occurring characters

ValueCountFrequency (%)
2 17680
80.6%
1 4249
 
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21929
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 17680
80.6%
1 4249
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21929
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 17680
80.6%
1 4249
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21929
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 17680
80.6%
1 4249
 
19.4%

Race
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2547
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean1.6209126
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.4 KiB
2025-02-20T07:31:23.575381image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98764004
Coefficient of variation (CV)0.6093111
Kurtosis10.33893
Mean1.6209126
Median Absolute Deviation (MAD)0
Skewness2.9415345
Sum52148
Variance0.97543284
MonotonicityNot monotonic
2025-02-20T07:31:23.650175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 17455
50.3%
2 12833
37.0%
6 1071
 
3.1%
3 708
 
2.0%
5 57
 
0.2%
4 48
 
0.1%
(Missing) 2547
 
7.3%
ValueCountFrequency (%)
1 17455
50.3%
2 12833
37.0%
3 708
 
2.0%
4 48
 
0.1%
5 57
 
0.2%
6 1071
 
3.1%
ValueCountFrequency (%)
6 1071
 
3.1%
5 57
 
0.2%
4 48
 
0.1%
3 708
 
2.0%
2 12833
37.0%
1 17455
50.3%

PosIntFinal
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size542.5 KiB
0
34091 
1
 
628

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

Length

2025-02-20T07:31:23.731159image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:23.808455image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34091
98.2%
1 628
 
1.8%

ClavOth
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing114
Missing (%)0.3%
Memory size542.5 KiB
0
34359 
1
 
246

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34605
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34359
99.0%
1 246
 
0.7%
(Missing) 114
 
0.3%

Length

2025-02-20T07:31:23.877668image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T07:31:23.943305image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34359
99.3%
1 246
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 34359
99.3%
1 246
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34359
99.3%
1 246
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34359
99.3%
1 246
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34359
99.3%
1 246
 
0.7%

Interactions

2025-02-20T07:30:39.745545image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:18.655525image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:21.596394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:24.112026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:26.316640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:29.223001image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:31.935473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:34.558774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:37.287114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:40.061375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:18.982464image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:21.901975image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:24.364174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:26.635504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:29.511160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:32.198210image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:34.898399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:37.455732image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:40.311769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:19.310503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:22.184790image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:24.630658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:26.953543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:29.805938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:32.465797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:35.169222image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:37.663489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:40.541138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:19.751553image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:22.518127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:24.794920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:27.251231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:30.129639image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:32.775666image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:35.493534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:38.278375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:40.811532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:20.041152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:22.817922image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:25.072960image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:27.736097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:30.455589image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:33.108917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:35.805953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:38.562684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:41.136195image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:20.355042image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:23.120038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:25.371132image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:27.977779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:30.775502image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:33.428030image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:36.081078image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:38.835799image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:41.442535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:20.678418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:23.347680image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:25.616255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:28.295974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:31.088123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:33.715566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:36.383544image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:39.014507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:41.773468image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:20.999673image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:23.691163image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:25.817452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:28.614404image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:31.349267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:34.051463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:36.723243image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:39.317318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:42.009470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:21.314498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:23.921650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:26.044435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:28.917039image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:31.625435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:34.259201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:37.040636image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-02-20T07:30:39.476974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-02-20T07:31:24.042174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AMSAMSAgitatedAMSOthAMSRepeatAMSSleepAMSSlowActNormAgeInMonthAmnesia_verbClavFaceClavFroClavNeckClavOccClavOthClavParClavTemDizzyDrugsEthnicityFontBulgGCSEyeGCSMotorGCSTotalGCSVerbalGenderHASeverityHAStartHemaLocHemaSizeHigh_impact_InjSevInjuryMechIntubatedLocLenNeuroDCranialNeuroDMotorNeuroDOthNeuroDReflexNeuroDSensoryOSIAbdomenOSICspineOSICutOSIExtremityOSIFlankOSIOthOSIPelvisParalyzedPosIntFinalRaceSFxBasHemSFxBasOtoSFxBasPerSFxBasRetSFxBasRhiSFxPalpDepressSedatedSeizLenSeizOccurVomitLastVomitNbrVomitStart
AMS1.0001.0001.0001.0001.0001.0000.5780.0540.2820.0360.0040.0240.0000.0090.0350.0660.1640.1180.0300.0160.3680.3230.3610.5230.0250.2520.1830.0620.0830.0740.0960.1690.2620.2000.1210.0870.1990.0790.1150.0240.0510.0000.0300.0920.0430.1310.2480.0660.0970.0960.0960.0970.0960.0880.1620.1090.1000.2190.1930.193
AMSAgitated1.0001.0000.1050.0000.2380.1380.0590.1210.0660.0000.0000.0300.0000.0160.0080.0000.0520.0420.0160.0000.0630.1390.1400.1900.0000.0930.0250.0360.0450.1090.1180.0200.0270.0000.0930.0150.0980.0000.0000.0000.0570.0480.0440.0370.0130.0000.0650.0360.0220.0330.0210.0230.0250.0650.0000.0150.0290.0350.0470.033
AMSOth1.0000.1051.0000.0500.2830.1200.0190.0160.0000.0190.0450.0000.0000.0260.0000.0000.0010.0180.0000.0160.1690.1570.1770.1730.0130.0280.0400.0360.0240.0000.0000.1260.0780.0680.0000.0880.0960.0660.0000.0000.0200.0270.0000.0060.0000.0670.0690.0450.0460.0530.0460.0470.0460.0220.0720.0260.0280.0530.0500.054
AMSRepeat1.0000.0000.0501.0000.1780.0000.0870.2780.2860.0500.0310.0110.0000.0000.0100.0000.0410.0390.0610.0000.0700.0650.0640.1180.0420.0560.0590.0600.0600.0560.2220.0480.1950.0000.0000.0290.0000.0000.0000.0000.0000.0180.0250.0890.0140.0350.0000.1100.0100.0000.0050.0000.0120.0120.0470.0000.0310.0880.0890.088
AMSSleep1.0000.2380.2830.1781.0000.0840.0410.2210.2280.0460.0300.0000.0000.0000.0470.0000.0520.0360.0000.0210.2050.1700.1890.2420.0330.0720.0400.0760.0440.1090.1620.1700.1690.1740.1290.0560.0310.0900.0000.0590.0020.0000.0000.0720.0750.1260.1130.0920.0510.0510.0530.0530.0520.0460.1260.0650.0660.2180.2210.218
AMSSlow1.0000.1380.1200.0000.0841.0000.1560.2880.0870.0000.0110.0340.0000.0000.0230.0180.1290.0790.0370.0180.1560.0850.1400.0880.0710.1070.0960.0430.0470.0910.2090.0940.1540.2640.0000.0000.1440.1260.0000.0780.0000.0000.0180.0240.0000.0710.0280.0450.0210.0090.0100.0080.0230.0460.0630.0390.0510.0050.0000.028
ActNorm0.5780.0590.0190.0870.0410.1561.0000.0400.2290.0160.0060.0210.0030.0000.0210.0460.1870.0640.0260.0170.2510.2180.2550.3410.0220.2520.1880.0490.0500.0330.0890.1020.1920.0780.1310.0730.1500.0520.0710.0000.0250.0460.0000.0290.0000.0750.1670.0770.0600.0590.0600.0590.0590.0480.0890.0850.0820.2550.2470.238
AgeInMonth0.0540.1210.0160.2780.2210.2880.0401.0000.2670.1280.1240.1170.0760.0080.0340.0520.2150.2030.0800.0510.030-0.005-0.0020.0350.1020.1840.2970.0840.0600.138-0.0320.0290.2960.0660.0000.0620.1410.0980.0800.1290.0330.0000.0590.2120.0590.0300.0270.0020.0200.0170.0170.0140.0190.0170.0300.0050.0140.0450.042-0.053
Amnesia_verb0.2820.0660.0000.2860.2280.0870.2290.2671.0000.0890.0310.0400.0180.0000.0190.0450.1060.1020.0320.0000.1140.0710.1060.2160.0390.1860.1600.0740.0660.0880.2150.0060.6030.0000.1530.0340.0000.0650.0560.0250.0270.0260.0000.1410.0660.0060.0960.1340.0310.0310.0310.0310.0320.0170.0350.1090.1150.0650.0470.053
ClavFace0.0360.0000.0190.0500.0460.0000.0160.1280.0891.0000.3360.0230.3310.0990.2600.1090.0000.0430.0000.0000.0390.0300.0390.0390.0190.0590.0470.3430.3000.0730.1970.0230.0910.1240.0000.0170.0000.0000.0560.0950.0320.0610.0410.0810.0000.0180.0000.0070.0460.0390.0460.0400.0410.0460.0230.0200.0120.0170.0190.016
ClavFro0.0040.0000.0450.0310.0300.0110.0060.1240.0310.3361.0000.0550.2440.0720.1680.0700.0000.0000.0200.0010.0060.0130.0170.0000.0310.0220.0060.5300.2520.0590.0980.0000.0430.0000.0590.0070.0880.0330.0000.0240.0000.0530.0180.0140.0000.0000.0000.0060.0230.0170.0240.0170.0170.0190.0000.0000.0000.0170.0130.016
ClavNeck0.0240.0300.0000.0110.0000.0340.0210.1170.0400.0230.0551.0000.0280.0120.0100.0070.0330.0390.0040.0000.0150.0220.0300.0200.0000.0660.0510.0590.0480.0410.1200.0130.0650.0970.0000.0380.0000.1200.0220.2720.0000.1140.1000.1160.0120.0070.0230.0220.0250.0210.0200.0190.0200.0000.0190.0000.0000.0100.0000.005
ClavOcc0.0000.0000.0000.0000.0000.0000.0030.0760.0180.3310.2440.0281.0000.0430.0580.0540.0000.0000.0170.0000.0040.0000.0100.0170.0370.0500.0490.6990.1580.0000.1160.0160.0130.0000.1330.0000.0690.0710.0000.0040.0000.0350.0880.0240.0000.0080.0260.0140.0220.0110.0180.0200.0130.0370.0000.0350.0260.0050.0000.010
ClavOth0.0090.0160.0260.0000.0000.0000.0000.0080.0000.0990.0720.0120.0431.0000.0380.0240.0070.0090.0170.0000.0130.0160.0160.0170.0000.0060.0110.0540.0210.0000.0150.0030.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0060.0200.0200.0260.0240.0210.0000.0030.0170.0000.0000.0020.000
ClavPar0.0350.0080.0000.0100.0470.0230.0210.0340.0190.2600.1680.0100.0580.0381.0000.0330.0210.0090.0150.0020.0460.0450.0450.0530.0390.0510.0460.5730.1400.0550.0620.0370.0390.0000.0620.0000.0910.0000.0000.0420.0290.0000.0220.0170.0000.0200.0830.0230.0290.0280.0290.0370.0280.0930.0200.0210.0190.0130.0170.000
ClavTem0.0660.0000.0000.0000.0000.0180.0460.0520.0450.1090.0700.0070.0540.0240.0331.0000.0360.0180.0040.0090.0550.0530.0620.0550.0160.0930.0790.3640.0880.0450.0670.0430.0500.0170.0000.0190.0000.0000.0000.0000.0000.0000.0400.0390.0210.0270.0680.0000.0530.0450.0510.0520.0490.0550.0290.0000.0000.0390.0360.039
Dizzy0.1640.0520.0010.0410.0520.1290.1870.2150.1060.0000.0000.0330.0000.0070.0210.0361.0000.0230.0000.0000.0490.0000.0240.0660.0000.3110.2690.0390.0310.0460.1780.0000.1040.0000.0650.0690.0000.1490.0000.1660.0000.0780.0000.0000.0360.0000.0200.0900.0170.0140.0150.0140.0160.0200.0000.0000.0100.1130.1100.111
Drugs0.1180.0420.0180.0390.0360.0790.0640.2030.1020.0430.0000.0390.0000.0090.0090.0180.0231.0000.0110.0000.0850.0680.0930.1110.0240.0440.0420.0160.0250.0230.1200.0040.1360.0350.0000.0330.0000.0000.0000.0000.0000.0000.0000.0550.0000.0110.0340.0190.0290.0240.0250.0230.0240.0000.0230.0060.0110.0110.0130.025
Ethnicity0.0300.0160.0000.0610.0000.0370.0260.0800.0320.0000.0200.0040.0170.0170.0150.0040.0000.0111.0000.0090.0000.0000.0000.0000.0000.0160.0170.0440.0350.0240.0890.0000.0230.0000.0460.0000.0000.0000.0320.0620.0050.0430.0290.0000.0000.0000.0130.3710.0080.0080.0100.0080.0110.0290.0000.0000.0000.0240.0130.022
FontBulg0.0160.0000.0160.0000.0210.0180.0170.0510.0000.0000.0010.0000.0000.0000.0020.0090.0000.0000.0091.0000.0770.0800.1040.0830.0000.0010.0000.0160.0170.0170.0210.0600.0300.1310.0520.0260.0960.0000.0000.0000.0000.0000.0000.0050.0000.0240.0510.0000.0280.0180.0210.0180.0180.0260.0460.0520.0510.0030.0030.000
GCSEye0.3680.0630.1690.0700.2050.1560.2510.0300.1140.0390.0060.0150.0040.0130.0460.0550.0490.0850.0000.0771.0000.5590.7020.5670.0220.0660.0380.0270.0480.0590.0780.7740.2460.4090.2620.2130.3830.1000.0820.0350.0640.0240.0880.1010.0930.6300.4850.0120.1190.1220.1210.1210.1230.0920.6000.1240.1050.0590.0590.054
GCSMotor0.3230.1390.1570.0650.1700.0850.218-0.0050.0710.0300.0130.0220.0000.0160.0450.0530.0000.0680.0000.0800.5591.0000.5890.5360.0000.030-0.0010.0260.0430.0660.0630.812-0.1880.4180.2930.2240.5000.1750.0860.0370.0490.0090.0830.0980.0950.7830.4790.0130.1340.1380.1370.1330.1350.0990.630-0.1530.1150.0390.043-0.043
GCSTotal0.3610.1400.1770.0640.1890.1400.255-0.0020.1060.0390.0170.0300.0100.0160.0450.0620.0240.0930.0000.1040.7020.5891.0000.6500.0200.054-0.0780.0280.0450.0640.0620.833-0.2240.4510.2890.2250.4490.1470.0800.0180.0810.0000.0880.1010.0990.7140.5130.0270.1360.1440.1400.1380.1430.1050.637-0.1250.1290.0550.055-0.075
GCSVerbal0.5230.1900.1730.1180.2420.0880.3410.0350.2160.0390.0000.0200.0170.0170.0530.0550.0660.1110.0000.0830.5670.5360.6501.0000.0070.0660.0350.0290.0470.0770.0650.8030.2140.4230.2780.2090.4370.1640.0870.0630.0550.0380.0930.1040.0770.6190.4800.0200.1270.1310.1290.1280.1290.0930.6060.1370.1040.0550.0530.046
Gender0.0250.0000.0130.0420.0330.0710.0220.1020.0390.0190.0310.0000.0370.0000.0390.0160.0000.0240.0000.0000.0220.0000.0200.0071.0000.0260.0260.0660.0420.0210.1460.0000.0360.0240.0000.0000.0000.0000.0670.0390.0000.0450.0000.0000.0630.0000.0000.0360.0050.0000.0000.0000.0040.0060.0000.0180.0000.0210.0210.020
HASeverity0.2520.0930.0280.0560.0720.1070.2520.1840.1860.0590.0220.0660.0500.0060.0510.0930.3110.0440.0160.0010.0660.0300.0540.0660.0261.0000.5160.0570.0570.0380.1110.0130.1040.0000.2050.0570.0000.1000.0000.2120.0270.0890.0410.0510.0150.0000.0990.0260.0330.0320.0320.0310.0320.0430.0000.0030.0050.0990.0950.093
HAStart0.1830.0250.0400.0590.0400.0960.1880.2970.1600.0470.0060.0510.0490.0110.0460.0790.2690.0420.0170.0000.038-0.001-0.0780.0350.0260.5161.0000.0570.0510.0270.0080.0000.1590.0000.1420.0380.1590.0000.0000.1780.0140.0980.0000.0320.0180.0000.065-0.0430.0290.0340.0260.0270.0260.0320.0000.0010.0090.0900.0870.160
HemaLoc0.0620.0360.0360.0600.0760.0430.0490.0840.0740.3430.5300.0590.6990.0540.5730.3640.0390.0160.0440.0160.0270.0260.0280.0290.0660.0570.0571.0000.5100.1100.1060.0380.0450.0880.1060.0170.1050.0000.0500.1070.0450.0970.0580.0560.0320.0180.1060.0260.0340.0290.0330.0300.0310.1070.0220.0110.0110.0230.0240.023
HemaSize0.0830.0450.0240.0600.0440.0470.0500.0600.0660.3000.2520.0480.1580.0210.1400.0880.0310.0250.0350.0170.0480.0430.0450.0470.0420.0570.0510.5101.0000.1020.0950.0640.0460.1830.1640.0310.1010.0170.0650.1040.0170.0860.0190.0570.0400.0400.1190.0250.0320.0340.0320.0330.0330.1540.0400.0120.0080.0220.0190.020
High_impact_InjSev0.0740.1090.0000.0560.1090.0910.0330.1380.0880.0730.0590.0410.0000.0000.0550.0450.0460.0230.0240.0170.0590.0660.0640.0770.0210.0380.0270.1100.1021.0000.7620.0640.0640.1220.1530.0110.0610.1110.0930.1090.0760.1180.0200.1540.0620.0450.1080.0250.0310.0300.0310.0300.0310.0430.0560.0240.0220.0430.0450.043
InjuryMech0.0960.1180.0000.2220.1620.2090.089-0.0320.2150.1970.0980.1200.1160.0150.0620.0670.1780.1200.0890.0210.0780.0630.0620.0650.1460.1110.0080.1060.0950.7621.0000.109-0.0800.1710.2570.0600.1820.1650.2160.2480.0690.1830.0920.3010.1590.0910.118-0.0020.0420.0420.0420.0440.0400.0190.103-0.0100.0220.0630.0610.018
Intubated0.1690.0200.1260.0480.1700.0940.1020.0290.0060.0230.0000.0130.0160.0030.0370.0430.0000.0040.0000.0600.7740.8120.8330.8030.0000.0130.0000.0380.0640.0640.1091.0000.3270.3320.2530.1480.3370.1350.0410.0260.0150.0150.0640.0820.1030.7810.3850.0360.1510.1590.1520.1520.1590.1230.7640.1430.1240.0540.0860.035
LocLen0.2620.0270.0780.1950.1690.1540.1920.2960.6030.0910.0430.0650.0130.0110.0390.0500.1040.1360.0230.0300.246-0.188-0.2240.2140.0360.1040.1590.0450.0460.064-0.0800.3271.0000.2920.3260.1170.4240.1210.1000.0400.0510.0580.0940.1860.1360.2110.281-0.0700.0740.0730.0730.0740.0730.0600.2180.1730.1050.0310.0260.013
NeuroDCranial0.2000.0000.0680.0000.1740.2640.0780.0660.0000.1240.0000.0970.0000.0000.0000.0170.0000.0350.0000.1310.4090.4180.4510.4230.0240.0000.0000.0880.1830.1220.1710.3320.2921.0000.0000.3870.1980.0000.1400.2400.2020.0000.1310.0000.0620.1150.3290.0000.2410.2110.2230.2020.2210.2850.1050.0000.0000.0250.0150.064
NeuroDMotor0.1210.0930.0000.0000.1290.0000.1310.0000.1530.0000.0590.0000.1330.0000.0620.0000.0650.0000.0460.0520.2620.2930.2890.2780.0000.2050.1420.1060.1640.1530.2570.2530.3260.0001.0000.4120.2640.2310.1600.0000.0550.0000.1460.2700.0000.1320.2770.0400.1030.0950.0980.0940.1000.1430.2050.1460.1630.0740.1150.107
NeuroDOth0.0870.0150.0880.0290.0560.0000.0730.0620.0340.0170.0070.0380.0000.0000.0000.0190.0690.0330.0000.0260.2130.2240.2250.2090.0000.0570.0380.0170.0310.0110.0600.1480.1170.3870.4121.0000.2040.4550.0000.0990.0000.0810.0350.0620.0100.0710.1500.0170.0520.0520.0520.0560.0590.0540.0640.0460.0430.0110.0110.013
NeuroDReflex0.1990.0980.0960.0000.0310.1440.1500.1410.0000.0000.0880.0000.0690.0000.0910.0000.0000.0000.0000.0960.3830.5000.4490.4370.0000.0000.1590.1050.1010.0610.1820.3370.4240.1980.2640.2041.0000.2240.1500.1410.0000.0000.1000.0960.0000.0870.2490.0000.2040.2160.2040.1910.2040.2620.1070.1830.2010.0590.0620.036
NeuroDSensory0.0790.0000.0660.0000.0900.1260.0520.0980.0650.0000.0330.1200.0710.0000.0000.0000.1490.0000.0000.0000.1000.1750.1470.1640.0000.1000.0000.0000.0170.1110.1650.1350.1210.0000.2310.4550.2241.0000.0000.1660.0000.0660.1080.1840.0000.0000.0530.0800.0000.0330.0450.0000.0000.0500.0570.0000.1090.0830.1140.086
OSIAbdomen0.1150.0000.0000.0000.0000.0000.0710.0800.0560.0560.0000.0220.0000.0000.0000.0000.0000.0000.0320.0000.0820.0860.0800.0870.0670.0000.0000.0500.0650.0930.2160.0410.1000.1400.1600.0000.1500.0001.0000.0420.0000.2300.0400.1410.0700.0000.0690.0800.0000.0000.0000.0000.0000.0240.0000.0000.0000.0530.0550.046
OSICspine0.0240.0000.0000.0000.0590.0780.0000.1290.0250.0950.0240.2720.0040.0000.0420.0000.1660.0000.0620.0000.0350.0370.0180.0630.0390.2120.1780.1070.1040.1090.2480.0260.0400.2400.0000.0990.1410.1660.0421.0000.0210.2990.0000.1340.0130.0170.0450.0420.0290.0290.0290.0290.0300.0390.0300.0000.0120.0440.0230.000
OSICut0.0510.0570.0200.0000.0020.0000.0250.0330.0270.0320.0000.0000.0000.0000.0290.0000.0000.0000.0050.0000.0640.0490.0810.0550.0000.0270.0140.0450.0170.0760.0690.0150.0510.2020.0550.0000.0000.0000.0000.0211.0000.0000.0000.0200.0200.0000.0780.0000.0200.0260.0170.0230.0430.1240.0170.0000.0000.0020.0000.022
OSIExtremity0.0000.0480.0270.0180.0000.0000.0460.0000.0260.0610.0530.1140.0350.0000.0000.0000.0780.0000.0430.0000.0240.0090.0000.0380.0450.0890.0980.0970.0860.1180.1830.0150.0580.0000.0000.0810.0000.0660.2300.2990.0001.0000.2400.4220.0840.0000.0000.0530.0340.0330.0490.0310.0350.0000.0000.0310.0370.0760.0770.071
OSIFlank0.0300.0440.0000.0250.0000.0180.0000.0590.0000.0410.0180.1000.0880.0000.0220.0400.0000.0000.0290.0000.0880.0830.0880.0930.0000.0410.0000.0580.0190.0200.0920.0640.0940.1310.1460.0350.1000.1080.0400.0000.0000.2401.0000.1510.0810.0440.0350.0170.0740.0770.0790.0730.0740.0000.0480.0000.0200.0000.0440.000
OSIOth0.0920.0370.0060.0890.0720.0240.0290.2120.1410.0810.0140.1160.0240.0000.0170.0390.0000.0550.0000.0050.1010.0980.1010.1040.0000.0510.0320.0560.0570.1540.3010.0820.1860.0000.2700.0620.0960.1840.1410.1340.0200.4220.1511.0000.0790.0650.0910.0380.0530.0540.0530.0530.0530.0280.0820.0100.0100.0560.0520.049
OSIPelvis0.0430.0130.0000.0140.0750.0000.0000.0590.0660.0000.0000.0120.0000.0000.0000.0210.0360.0000.0000.0000.0930.0950.0990.0770.0630.0150.0180.0320.0400.0620.1590.1030.1360.0620.0000.0100.0000.0000.0700.0130.0200.0840.0810.0791.0000.0610.0810.0570.0670.0790.0680.0770.0710.0300.0810.0730.0600.0000.0000.000
Paralyzed0.1310.0000.0670.0350.1260.0710.0750.0300.0060.0180.0000.0070.0080.0000.0200.0270.0000.0110.0000.0240.6300.7830.7140.6190.0000.0000.0000.0180.0400.0450.0910.7810.2110.1150.1320.0710.0870.0000.0000.0170.0000.0000.0440.0650.0611.0000.2710.0270.1030.1050.1080.1100.1110.0610.7740.1380.1050.0470.0710.030
PosIntFinal0.2480.0650.0690.0000.1130.0280.1670.0270.0960.0000.0000.0230.0260.0180.0830.0680.0200.0340.0130.0510.4850.4790.5130.4800.0000.0990.0650.1060.1190.1080.1180.3850.2810.3290.2770.1500.2490.0530.0690.0450.0780.0000.0350.0910.0810.2711.0000.0330.2340.2310.2290.2310.2300.2200.2960.1260.1090.0910.0890.080
Race0.0660.0360.0450.1100.0920.0450.0770.0020.1340.0070.0060.0220.0140.0060.0230.0000.0900.0190.3710.0000.0120.0130.0270.0200.0360.026-0.0430.0260.0250.025-0.0020.036-0.0700.0000.0400.0170.0000.0800.0800.0420.0000.0530.0170.0380.0570.0270.0331.0000.0140.0150.0130.0140.0150.0000.035-0.0200.0110.0540.054-0.098
SFxBasHem0.0970.0220.0460.0100.0510.0210.0600.0200.0310.0460.0230.0250.0220.0200.0290.0530.0170.0290.0080.0280.1190.1340.1360.1270.0050.0330.0290.0340.0320.0310.0420.1510.0740.2410.1030.0520.2040.0000.0000.0290.0200.0340.0740.0530.0670.1030.2340.0141.0000.7160.7920.7220.7250.0750.1180.0000.0100.0360.0310.026
SFxBasOto0.0960.0330.0530.0000.0510.0090.0590.0170.0310.0390.0170.0210.0110.0200.0280.0450.0140.0240.0080.0180.1220.1380.1440.1310.0000.0320.0340.0290.0340.0300.0420.1590.0730.2110.0950.0520.2160.0330.0000.0290.0260.0330.0770.0540.0790.1050.2310.0150.7161.0000.7130.7080.7070.0860.1180.0100.0170.0300.0220.025
SFxBasPer0.0960.0210.0460.0050.0530.0100.0600.0170.0310.0460.0240.0200.0180.0260.0290.0510.0150.0250.0100.0210.1210.1370.1400.1290.0000.0320.0260.0330.0320.0310.0420.1520.0730.2230.0980.0520.2040.0450.0000.0290.0170.0490.0790.0530.0680.1080.2290.0130.7920.7131.0000.7100.7130.0760.1200.0000.0100.0320.0250.024
SFxBasRet0.0970.0230.0470.0000.0530.0080.0590.0140.0310.0400.0170.0190.0200.0240.0370.0520.0140.0230.0080.0180.1210.1330.1380.1280.0000.0310.0270.0300.0330.0300.0440.1520.0740.2020.0940.0560.1910.0000.0000.0290.0230.0310.0730.0530.0770.1100.2310.0140.7220.7080.7101.0000.7100.0760.1220.0050.0130.0320.0220.024
SFxBasRhi0.0960.0250.0460.0120.0520.0230.0590.0190.0320.0410.0170.0200.0130.0210.0280.0490.0160.0240.0110.0180.1230.1350.1430.1290.0040.0320.0260.0310.0330.0310.0400.1590.0730.2210.1000.0590.2040.0000.0000.0300.0430.0350.0740.0530.0710.1110.2300.0150.7250.7070.7130.7101.0000.0800.1200.0000.0090.0320.0220.024
SFxPalpDepress0.0880.0650.0220.0120.0460.0460.0480.0170.0170.0460.0190.0000.0370.0000.0930.0550.0200.0000.0290.0260.0920.0990.1050.0930.0060.0430.0320.1070.1540.0430.0190.1230.0600.2850.1430.0540.2620.0500.0240.0390.1240.0000.0000.0280.0300.0610.2200.0000.0750.0860.0760.0760.0801.0000.0690.0210.0190.0100.0140.011
Sedated0.1620.0000.0720.0470.1260.0630.0890.0300.0350.0230.0000.0190.0000.0030.0200.0290.0000.0230.0000.0460.6000.6300.6370.6060.0000.0000.0000.0220.0400.0560.1030.7640.2180.1050.2050.0640.1070.0570.0000.0300.0170.0000.0480.0820.0810.7740.2960.0350.1180.1180.1200.1220.1200.0691.0000.1480.1240.0530.0720.036
SeizLen0.1090.0150.0260.0000.0650.0390.0850.0050.1090.0200.0000.0000.0350.0170.0210.0000.0000.0060.0000.0520.124-0.153-0.1250.1370.0180.0030.0010.0110.0120.024-0.0100.1430.1730.0000.1460.0460.1830.0000.0000.0000.0000.0310.0000.0100.0730.1380.126-0.0200.0000.0100.0000.0050.0000.0210.1481.0000.5880.0190.0100.006
SeizOccur0.1000.0290.0280.0310.0660.0510.0820.0140.1150.0120.0000.0000.0260.0000.0190.0000.0100.0110.0000.0510.1050.1150.1290.1040.0000.0050.0090.0110.0080.0220.0220.1240.1050.0000.1630.0430.2010.1090.0000.0120.0000.0370.0200.0100.0600.1050.1090.0110.0100.0170.0100.0130.0090.0190.1240.5881.0000.0140.0050.013
VomitLast0.2190.0350.0530.0880.2180.0050.2550.0450.0650.0170.0170.0100.0050.0000.0130.0390.1130.0110.0240.0030.0590.0390.0550.0550.0210.0990.0900.0230.0220.0430.0630.0540.0310.0250.0740.0110.0590.0830.0530.0440.0020.0760.0000.0560.0000.0470.0910.0540.0360.0300.0320.0320.0320.0100.0530.0190.0141.0000.5300.559
VomitNbr0.1930.0470.0500.0890.2210.0000.2470.0420.0470.0190.0130.0000.0000.0020.0170.0360.1100.0130.0130.0030.0590.0430.0550.0530.0210.0950.0870.0240.0190.0450.0610.0860.0260.0150.1150.0110.0620.1140.0550.0230.0000.0770.0440.0520.0000.0710.0890.0540.0310.0220.0250.0220.0220.0140.0720.0100.0050.5301.0000.506
VomitStart0.1930.0330.0540.0880.2180.0280.238-0.0530.0530.0160.0160.0050.0100.0000.0000.0390.1110.0250.0220.0000.054-0.043-0.0750.0460.0200.0930.1600.0230.0200.0430.0180.0350.0130.0640.1070.0130.0360.0860.0460.0000.0220.0710.0000.0490.0000.0300.080-0.0980.0260.0250.0240.0240.0240.0110.0360.0060.0130.5590.5061.000

Missing values

2025-02-20T07:30:42.652742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T07:30:43.948500image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-20T07:30:47.790473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

InjuryMechHigh_impact_InjSevAmnesia_verbLocLenSeizOccurSeizLenActNormHASeverityHAStartVomitNbrVomitStartVomitLastDizzyIntubatedParalyzedSedatedGCSEyeGCSVerbalGCSMotorGCSTotalAMSAMSAgitatedAMSSleepAMSSlowAMSRepeatAMSOthSFxPalpDepressFontBulgSFxBasHemSFxBasOtoSFxBasPerSFxBasRetSFxBasRhiHemaLocHemaSizeClavFaceClavNeckClavFroClavOccClavParClavTemNeuroDMotorNeuroDSensoryNeuroDCranialNeuroDReflexNeuroDOthOSIExtremityOSICutOSICspineOSIFlankOSIAbdomenOSIPelvisOSIOthDrugsAgeInMonthGenderEthnicityRacePosIntFinalClavOth
PatNum
4309011200001120000000456150<NA><NA><NA><NA><NA>000000022000100<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1012412200
289211200001000000000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>10001000021022200
123582<NA>000100000<NA>000456150<NA><NA><NA><NA><NA>000000011<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>102222100
206819200001220000000456150<NA><NA><NA><NA><NA>000000012001000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>10652<NA>100
4031792<NA>0001<NA><NA>121<NA>000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>10512100
7380132<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>101312100
12013300001120001000456150<NA><NA><NA><NA><NA>000000000100100<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>101751<NA>200
2517121<NA><NA><NA>1230000000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1020811<NA>00
301751200001000000000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>1000010005922100
302927100001000000000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>106312100
InjuryMechHigh_impact_InjSevAmnesia_verbLocLenSeizOccurSeizLenActNormHASeverityHAStartVomitNbrVomitStartVomitLastDizzyIntubatedParalyzedSedatedGCSEyeGCSVerbalGCSMotorGCSTotalAMSAMSAgitatedAMSSleepAMSSlowAMSRepeatAMSOthSFxPalpDepressFontBulgSFxBasHemSFxBasOtoSFxBasPerSFxBasRetSFxBasRhiHemaLocHemaSizeClavFaceClavNeckClavFroClavOccClavParClavTemNeuroDMotorNeuroDSensoryNeuroDCranialNeuroDReflexNeuroDOthOSIExtremityOSICutOSICspineOSIFlankOSIAbdomenOSIPelvisOSIOthDrugsAgeInMonthGenderEthnicityRacePosIntFinalClavOth
PatNum
2589652<NA>4000<NA><NA>000<NA>1111113100000000000000101000000010010000017612110
110331220000100000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>10651<NA><NA>00
35797100001000000000456150<NA><NA><NA><NA><NA>000000000100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>107512200
1737092<NA>0001<NA><NA>222<NA>000456150<NA><NA><NA><NA><NA>000000034100001<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>101011100
287894200001000000000456150<NA><NA><NA><NA><NA>000000012100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1013212200
108801200001220000000456150<NA><NA><NA><NA><NA>000000032000010<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1019312200
2669683<NA>0000<NA><NA>122<NA>000456150<NA><NA><NA><NA><NA>00<NA><NA><NA><NA><NA>00100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>102112100
3678611201001220000000456150<NA><NA><NA><NA><NA>001121100100000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1<NA>2122<NA>200
4053613200001<NA><NA>000<NA>00045615101000000000012101000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>106212100
1593212200001120001000456150<NA><NA><NA><NA><NA>000000000001000<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1018511<NA>00

Duplicate rows

Most frequently occurring

InjuryMechHigh_impact_InjSevAmnesia_verbLocLenSeizOccurSeizLenActNormHASeverityHAStartVomitNbrVomitStartVomitLastDizzyIntubatedParalyzedSedatedGCSEyeGCSVerbalGCSMotorGCSTotalAMSAMSAgitatedAMSSleepAMSSlowAMSRepeatAMSOthSFxPalpDepressFontBulgSFxBasHemSFxBasOtoSFxBasPerSFxBasRetSFxBasRhiHemaLocHemaSizeClavFaceClavNeckClavFroClavOccClavParClavTemNeuroDMotorNeuroDSensoryNeuroDCranialNeuroDReflexNeuroDOthOSIExtremityOSICutOSICspineOSIFlankOSIAbdomenOSIPelvisOSIOthDrugsAgeInMonthGenderEthnicityRacePosIntFinalClavOth# duplicates
23982<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1001220010
24982<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1011220010
29182<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>106122009
25482<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>1012<NA>2007
25582<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>102122007
26682<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>103221007
28682<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>105222007
31182<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>109122007
31282<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>109222007
24482<NA>0001<NA><NA>000<NA>000456150<NA><NA><NA><NA><NA>000000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA>100222006